# COBRA-Skills：用上下文赌博机引导智能体技能进化

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-10 08:00
- AIHOT 分数：41
- AIHOT 链接：https://aihot.news/items/cmu0nreon0380rox2qsnjnn72
- 原文链接：https://arxiv.org/abs/2609.11682

## AI 摘要

COBRA-Skills 将 LLM 智能体技能优化建模为动态候选空间上的预算化序列优化，结合上下文赌博机优先级排序与基于执行证据的技能进化。在六个异构智能体基准和三个目标模型上，其平均性能优于对比方法，优化成本较 SkillOpt 降低 55–58%，每个基准仅用 50 个优化样本。该方法对智能体 harness 变化保持稳健，目标模型自身也可用于技能生成与精炼。

## 正文

Large language model (LLM) agents can benefit from reusable skills distilled from prior task experience, yet existing skill optimization methods often rely on costly execution-based evaluation and substantial task data. We introduce COBRA-Skills, an efficient framework that formulates skill optimization as budgeted sequential optimization over a dynamically evolving candidate space. COBRA-Skills couples contextual-bandit-guided prioritization with evidence-grounded skill evolution, selectively allocating evaluations to promising or informative candidates while continually refining the skill population from execution feedback.

Across six heterogeneous agent benchmarks and three target models, COBRA-Skills consistently achieves the strongest average performance among compared methods, while reducing optimization cost by 55--58% relative to SkillOpt and using only 50 unique optimization examples per benchmark. Further analyses show that COBRA-Skills remains robust to changes in the agent harness and performs effectively when the target model itself is used for skill generation and refinement.